Blender as a tool for generating synthetic data

Rafał Sieczka; Maciej Pańczyk

doi:10.35784/jcsi.2086

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Published: Sep 30, 2020

DOI: https://doi.org/10.35784/jcsi.2086

Issue Vol. 16 (2020)

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DOI

https://doi.org/10.35784/jcsi.2086

Authors

Rafał Sieczka

rafal.sieczka@pollub.edu.pl

Politechnika Lubelska, Poland

Maciej Pańczyk

maciej.panczyk@pollub.edu.pl

Abstract

Acquiring data for neural network training is an expensive and labour-intensive task, especially when such data is
difficult to access. This article proposes the use of 3D Blender graphics software as a tool to automatically generate
synthetic image data on the example of price labels. Using the fastai library, price label classifiers were trained on
a set of synthetic data, which were compared with classifiers trained on a real data set. The comparison of the results
showed that it is possible to use Blender to generate synthetic data. This allows for a significant acceleration of the
data acquisition process and consequently, the learning process of neural networks.

Keywords:

artificial neural networks, convolutional neural network, synthetic data, blender

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Sieczka, R., & Pańczyk, M. (2020). Blender as a tool for generating synthetic data. Journal of Computer Sciences Institute, 16, 227–232. https://doi.org/10.35784/jcsi.2086

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